Directum pitches workflow agents as a practical way to bring AI into business processes

Directum has proposed a more practical way for companies to adopt AI. Rather than fully autonomous "digital employees," it is betting on workflow agents that run on predefined logic inside the corporate system. The company says this approach removes the main obstacles businesses face: fear of a "black box," the risk of data leaks and unclear ROI.

Why not autopilot

Directum starts from a fairly conservative market demand. Mid-sized and large companies don't want to build processes on external cloud services when contracts, correspondence and internal policies are involved. They expect on-premises deployment, predictable integrations and clear limits on data access.

So instead of demo AI agents running "somewhere else," the company builds embedded assistants inside Directum RX. They run within the corporate perimeter and draw on internal documents via RAG. As a result, the agent works not with abstract knowledge but with the context of a specific organization: local regulations, instructions, email history and the role-based access model.

An employee can paste a link to an incoming email directly in the system interface, ask the agent to find similar cases and have it draft a reply. Crucially, the human doesn't drop out of the process: they review the output and keep the final decision, while the machine takes over search, cross-checking and preparatory routine.

How the workflow works

Directum describes the workflow agent as an intermediate but already useful class of AI agent. It doesn't try to become a universal "digital employee" that can sort out any ambiguous situation on its own. Its job is simpler and more practical: follow a predefined route, complete standard steps and hand the process to a human if the task falls outside the permitted scenario.

That controllability is exactly what makes the approach appealing to businesses that want predictable results, not a wow effect.

• extract data from a contract and identify key terms;
• check banking details, the amount and basic constraints against company rules;
• automatically route a standard document through the right approval branch;
• if there is a risk or non-standard wording, stop the flow and send the document for manual review with an explanation.

Contracts make this especially clear. The agent can extract data from a document, check the amount and banking details, determine whether the contract fits corporate rules and immediately flag questionable wording as a risk.

This gives businesses transparency: you can see which rule fired, why a document went to manual review and exactly where a human needs to step in. For compliance teams, lawyers and managers, this scenario is far less nerve-racking than rolling out a fully autonomous "black box."

The numbers and the next step

In one Directum case, the agent kicks in as soon as a contract is uploaded to the system. It runs a chain of preconfigured prompts, checks the text against corporate checklists for different counterparty types and verifies payment terms and transfer of ownership.

Then branching takes over. For contracts under 1 million rubles, a standard path is possible: the agent makes the decision itself under an agreed scenario and notifies an employee. For amounts above 1 million, it produces a structured report listing discrepancies and critical clauses.

The numbers here are operational, not experimental: initial review time dropped from 30 to 5 minutes, annual payroll savings reached about 4.8 million rubles, and review accuracy, according to the company, was 95%. This freed two staff lawyers from a stream of routine tasks and let them shift to complex contracts.

Directum sees the next step in "orchestrators" that will coordinate several narrow agents at once — for contracts, invoices and deadlines.

"AI runs the process; a human oversees it and makes the final

decisions on exceptions."

What this means

The enterprise AI market is gradually moving away from the idea of instantly replacing people with fully autonomous agents and shifting toward a more pragmatic model. For businesses, the path is simple: pick one overloaded process, hand the routine to a workflow agent, measure the savings and only then scale the approach to other operations.